PROJECT SUMMARY Progressive aging-related cognitive declines are associated with limitations in self-care and functional independence, deteriorating physical health, and impending dementia and mortality, even among the otherwise healthy. Identifying and understanding the neurodegenerative processes that underlie cognitive aging is key to developing interventions to prevent or ameliorate cognitive decline. Disconnection theories of aging specifically implicate weakening of structural brain connectivity as a key mechanism of cognitive decline, but until recently, diffusion MRI data and connectomic methods needed to rigorously test such theories have been lacking. To expedite understanding how aging-related changes in the human structural connectome relate to aging-related cognitive declines, we will apply the latest connectomic and multivariate data analysis methods to existing data from two highly unique datasets: (1) The UK Biobank, a cross-sectional sample of ~10,000 40-75 year old adults, who have undergone diffusion MRI scanning, have been measured with multiple cognitive tests, and have provided extensive sociodemographic and medical information; and (2) The Lothian Birth Cohort of 1936, a narrow-age cohort of older adults (baseline age = 73 years; N = 731) who have undergone diffusion MRI scanning, have been measured with multiple cognitive tests, and have provided extensive sociodemographic and medical information on each of three separate occasions, each separated by three years. Using recently developed graph-theoretic models, we will construct structural brain connectome networks for each participant's diffusion MRI data at each wave and extract indices reflective of network topology within several specific networks of interest (NOIs) identified ex ante. We will also identify topologically central hub regions that disproportionately govern efficiency within each individual's connectome network. We will apply cross-sectional and longitudinal structural equation models to examine aging-related transformations in network indices, examine concurrent and longitudinal coupling between network indices and cognitive abilities, and test predictors of levels and changes in network indices and cognitive abilities. This will allow us to contrast the predictive utility of the selected NOIs for cognitive aging and to identify specific features of network architecture involved in cognitive aging and mediate the effects of demographic, medical, and lifestyle risk factors for cognitive aging. We additionally implement machine- learning methods to estimate an upper bound of prediction of cognitive aging from network indices, and identify novel features of network topology as candidate mechanisms of cognitive decline. The availability of two uniquely large and well-characterized datasets will allow us to ensure that findings are rigorous and reproducible using within sample (holdout) and between sample cross-validation. For all aims, we will place considerable emphasis on testing for incremental validity of network indices relative to both conventional structural neuroanatomical measures and topologically nave summary indices of network integrity.